Update app.py

app.py

@@ -1,65 +1,11 @@
-# -*- coding: utf-8 -*-
-"""app.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1qIFntwH-_zF7GkQbgjKoXMXnQpZ4HVse
-"""
-"""
-import gradio as gr
-from transformers import AutoTokenizer,  AutoModelForSequenceClassification
-
-# Load the base model
-base_model_name = "Preetham04/sentiment-analysis"
-tokenizer = AutoTokenizer.from_pretrained(base_model_name)
-model =  AutoModelForSequenceClassification.from_pretrained(base_model_name)
-
-# Load the adapter configuration and model files
-adapter_config_path = "config.json"
-adapter_model_path = "model.safetensors"
-
-# Load the adapter into the model
-adapter_name = "custom_adapter"  # Define your adapter name
-model.load_adapter(config_path=adapter_config_path, adapter_path=adapter_model_path, adapter_name=adapter_name)
-
-# Activate the adapter
-model.set_active_adapters(adapter_name)
-
-st.title("🤖 Chatbot with Adapter-Enhanced Model")
-st.write("Interact with your custom adapter-enhanced model. Type a message and get responses!")
-
-# Initialize or retrieve the chat history
-if 'history' not in st.session_state:
-    st.session_state['history'] = []
-
-# Initialize Gradio
-chatbot = Gradio(model=model, tokenizer=tokenizer)
-
-# Define responses for greetings
-@chatbot.on_event("welcome")
-def welcome_handler(payload):
-    return "Welcome! Type a message and get responses from the chatbot."
-
-# Define responses for user messages
-@chatbot.on_message
-def message_handler(payload):
-    user_input = payload["message"]
-    response = chatbot.generate_response(user_input)
-    return response
-
-# Run Gradio
-if __name__ == "__main__":
-    chatbot.run()
-"""
 import gradio as gr
 from transformers import pipeline
 
 pipeline = pipeline(task="text-classification", model="Preetham04/sentiment-analysis")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+def predict(input_text):
+    predictions = pipeline(input_text)
+    return input_text, {p["label"]: p["score"] for p in predictions} 
 
 gradio_app = gr.Interface(
     predict,